Method and system for providing low density parity check (LDPC) encoding

ABSTRACT

An approach is provided for a method of encoding structure Low Density Parity Check (LDPC) codes. Memory storing information representing a structured parity check Matrix of Low Density Parity Check (LDPC) codes is accessed during the encoding process. The information is organized in tabular form, wherein each row represents occurrences of one Values within a first column of a group of columns of the parity check matrix. The rows correspond to groups of columns of the parity check matrix, wherein subsequent columns within each of the groups are derived according to a predetermined operation. An LDPC coded signal is output based on the stored information representing the parity check matrix.

RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No. 10/613,823 filed Jul. 3, 2003, entitled “Method and System for Providing Low Density Parity Check (LDPC) Encoding”, which is related to, and claims the benefit of the earlier filing date under 35 U.S.C. § 119(e) of, U.S. Provisional Patent Application (Ser. No. 60/393,457) filed Jul. 3, 2002 (Attorney Docket: PD-202095), entitled “Code Design and Implementation Improvements for Low Density Parity Check Codes,” U.S. Provisional Patent Application (Ser. No. 60/398,760) filed Jul. 26, 2002 (Attorney Docket: PD-202101), entitled “Code Design and Implementation Improvements for Low Density Parity Check Codes,” U.S. Provisional Patent Application (Ser. No. 60/403,812) filed Aug. 15, 2002 (Attorney Docket: PD-202105), entitled “Power and Bandwidth Efficient Modulation and Coding Scheme for Direct Broadcast Satellite and Broadcast Satellite Communications,” U.S. Provisional Patent Application (Ser. No. 60/421,505), filed Oct. 25, 2002 (Attorney Docket: PD-202101), entitled “Method and System for Generating Low Density Parity Check Codes,” U.S. Provisional Patent Application (Ser. No. 60/421,999), filed Oct. 29, 2002 (Attorney Docket: PD-202105), entitled “Satellite Communication System Utilizing Low Density Parity Check Codes,” U.S. Provisional Patent Application (Ser. No. 60/423,710), filed Nov. 4, 2002 (Attorney Docket: PD-202101), entitled “Code Design and Implementation Improvements for Low Density Parity Check Codes,” U.S. Provisional Patent Application (Ser. No. 60/440,199) filed Jan. 15, 2003 (Attorney Docket: PD-203009), entitled “Novel Solution to Routing Problem in Low Density Parity Check Decoders,” U.S. Provisional Patent Application (Ser. No. 60/447,641) filed Feb. 14, 2003 (Attorney Docket: PD-203016), entitled “Low Density Parity Check Code Encoder Design,” U.S. Provisional Patent Application (Ser. No. 60/456,220) filed Mar. 20, 2003 (Attorney Docket: PD-203021), entitled “Description LDPC and BCH Encoders,” U.S. Provisional Patent Application filed May 9, 2003 (Attorney Docket: PD-203030), entitled “Description LDPC and BCH Encoders,” U.S. Provisional Patent Application filed Jun. 24, 2003 (Attorney Docket: PD-203044), entitled “Description LDPC and BCH Encoders,” and U.S. Provisional Patent Application filed Jun. 24, 2003 (Attorney Docket: PD-203059), entitled “Description LDPC and BCH Encoders”; the entireties of which are incorporated herein by reference.

FIELD OF THE INVENTION

The present invention relates to communication systems, and more particularly to coded systems.

BACKGROUND OF THE INVENTION

Communication systems employ coding to ensure reliable communication across noisy communication channels. These communication channels exhibit a fixed capacity that can be expressed in terms of bits per symbol at certain signal to noise ratio (SNR), defining a theoretical upper limit (known as the Shannon limit). As a result, coding design has aimed to achieve rates approaching this Shannon limit. One such class of codes that approach the Shannon limit is Low Density Parity Check (LDPC) codes.

Traditionally, LDPC codes have not been widely deployed because of a number of drawbacks. One drawback is that the LDPC encoding technique is highly complex. Encoding an LDPC code using its generator matrix would require storing a very large, non-sparse matrix. Additionally, LDPC codes require large blocks to be effective; consequently, even though parity check matrices of LDPC codes are sparse, storing these matrices is problematic.

From an implementation perspective, a number of challenges are confronted. For example, storage is an important reason why LDPC codes have not become widespread in practice. Also, a key challenge in LDPC code implementation has been how to achieve the connection network between several processing engines (nodes) in the decoder. Further, the computational load in the decoding process, specifically the check node operations, poses a problem.

Therefore, there is a need for an LDPC communication system that employs simple encoding and decoding processes. There is also a need for using LDPC codes efficiently to support high data rates, without introducing greater complexity. There is also a need to improve performance of LDPC encoders and decoders. There is also a need to minimize storage requirements for implementing LDPC coding. There is a further need for a scheme that simplifies the communication between processing nodes in the LDPC decoder.

SUMMARY OF THE INVENTION

These and other needs are addressed by the present invention, wherein an approach for encoding structured Low Density Parity Check (LDPC) codes is provided. Structure of the LDPC codes is provided by restricting portion part of the parity check matrix to be lower triangular and/or satisfying other requirements such that the communication between bit nodes and check nodes of the decoder is simplified. Memory storing information representing the structured parity check matrix is accessed. The information is organized in tabular form, wherein each row represents occurrences of one values within a first column of a group of columns of the parity check matrix. The rows correspond to groups of columns of the parity check matrix, wherein subsequent columns within each of the groups are derived according to a predetermined operation (e.g., cyclic shift, addition, etc.). An LDPC coded signal based on the stored information representing the parity check matrix. According to one embodiment of the present invention, a Bose Chaudhuri Hocquenghem (BCH) encoder is utilized by the transmitter to encode an input signal using BCH codes, wherein the output LDPC coded signal corresponding to the input signal represents a code having an outer BCH code and an inner LDPC code. Further, a cyclic redundancy check (CRC) encoder is supplied to encode the input signal according to a CRC code. The approach advantageously provides expedient encoding as well as decoding of LDPC codes.

According to one aspect of an embodiment of the present invention, a method of encoding is disclosed. The method includes accessing memory storing information representing a structured parity check matrix of Low Density Parity Check (LDPC) codes. The information is organized in tabular form, wherein each row represents occurrences of one values within a first column of a group of columns of the parity check matrix. The rows correspond to groups of columns of the parity check matrix, wherein subsequent columns within each of the groups are derived according to a predetermined operation. The method also includes outputting an LDPC coded signal based on the stored information representing the parity check matrix.

According to another aspect of an embodiment of the present invention, an encoder for generating Low Density Parity Check (LDPC) codes disclosed. The encoder includes memory storing information representing a structured parity check matrix of the LDPC codes. The information is organized in tabular form, wherein each row represents occurrences of one values within a first column of a group of columns of the parity check matrix. The rows correspond to groups of columns of the parity check matrix, wherein subsequent columns within each of the groups are derived according to a predetermined operation. The encoder also includes means for retrieving the stored information representing the parity check matrix to output an LDPC coded signal.

According to another aspect of an embodiment of the present invention, a transmitter utilizing Low Density Parity Check (LDPC) coding disclosed. The transmitter includes memory storing information representing a structured parity check matrix of the LDPC codes, the information being organized in tabular form, wherein each row represents occurrences of one values within a first column of a group of columns of the parity check matrix. The rows correspond to groups of columns of the parity check matrix, wherein subsequent columns within each of the groups are derived according to a predetermined operation. The transmitter also includes an LDPC encoder configured to access the stored information from the memory to output an LDPC coded signal.

Still other aspects, features, and advantages of the present invention are readily apparent from the following detailed description, simply by illustrating a number of particular embodiments and implementations, including the best mode contemplated for carrying out the present invention. The present invention is also capable of other and different embodiments, and its several details can be modified in various obvious respects, all without departing from the spirit and scope of the present invention. Accordingly, the drawing and description are to be regarded as illustrative in nature, and not as restrictive.

BRIEF DESCRIPTION OF THE DRAWINGS

The present invention is illustrated by way of example, and not by way of limitation, in the figures of the accompanying drawings and in which like reference numerals refer to similar elements and in which:

FIG. 1 is a diagram of a communications system configured to utilize Low Density Parity Check (LDPC) codes, according to an embodiment of the present invention;

FIGS. 2A and 2B are diagrams of exemplary LDPC encoders deployed in the transmitter of FIG. 1;

FIG. 3 is a diagram of an exemplary receiver in the system of FIG. 1;

FIG. 4 is a diagram of a sparse parity check matrix, in accordance with an embodiment of the present invention;

FIG. 5 is a diagram of a bipartite graph of an LDPC code of the matrix of FIG. 4;

FIG. 6 is a diagram of a sub-matrix of a sparse parity check matrix, wherein the sub-matrix contains parity check values restricted to the lower triangular region, according to an embodiment of the present invention;

FIG. 7 is a graph showing performance between codes utilizing unrestricted parity check matrix (H matrix) versus restricted H matrix having a sub-matrix as in FIG. 6;

FIGS. 8A and 8B are, respectively, a diagram of a non-Gray 8-PSK modulation scheme, and a Gray 8-PSK modulation, each of which can be used in the system of FIG. 1;

FIG. 9 is a graph showing performance between codes utilizing Gray labeling versus non-Gray labeling;

FIG. 10 is a flow chart of the operation of the LDPC decoder using non-Gray mapping, according to an embodiment of the present invention;

FIG. 11 is a flow chart of the operation of the LDPC decoder of FIG. 3 using Gray mapping, according to an embodiment of the present invention;

FIGS. 12A-12C are diagrams of the interactions between the check nodes and the bit nodes in a decoding process, according to an embodiment of the present invention;

FIGS. 13A and 13B are flowcharts of processes for computing outgoing messages between the check nodes and the bit nodes using, respectively, a forward-backward approach and a parallel approach, according to various embodiments of the present invention;

FIGS. 14A-14 are graphs showing simulation results of LDPC codes generated in accordance with various embodiments of the present invention;

FIGS. 15A and 15B are diagrams of the top edge and bottom edge, respectively, of memory organized to support structured access as to realize randomness in LDPC coding, according to an embodiment of the present invention; and

FIG. 16 is a diagram of a computer system that can perform the processes of encoding and decoding of LDPC codes, in accordance with embodiments of the present invention.

DESCRIPTION OF THE PREFERRED EMBODIMENT

A system, method, and software for efficiently decoding structured Low Density Parity Check (LDPC) codes are described. In the following description, for the purposes of explanation, numerous specific details are set forth in order to provide a thorough understanding of the present invention. It is apparent, however, to one skilled in the art that the present invention may be practiced without these specific details or with an equivalent arrangement. In other instances, well-known structures and devices are shown in block diagram form in order to avoid unnecessarily obscuring the present invention.

FIG. 1 is a diagram of a communications system configured to utilize Low Density Parity Check (LDPC) codes, according to an embodiment of the present invention. A digital communications system 100 includes a transmitter 101 that generates signal waveforms across a communication channel 103 to a receiver 105. In this discrete communications system 100, the transmitter 101 has a message source that produces a discrete set of possible messages; each of the possible messages has a corresponding signal waveform. These signal waveforms are attenuated, or otherwise altered, by communications channel 103. To combat the noise channel 103, LDPC codes are utilized.

The LDPC codes that are generated by the transmitter 101 enable high speed implementation without incurring any performance loss. These structured LDPC codes output from the transmitter 101 avoid assignment of a small number of check nodes to the bit nodes already vulnerable to channel errors by virtue of the modulation scheme (e.g., 8-PSK).

Such LDPC codes have a parallelizable decoding algorithm (unlike turbo codes), which advantageously involves simple operations such as addition, comparison and table look-up. Moreover, carefully designed LDPC codes do not exhibit any sign of error floor.

According to one embodiment of the present invention, the transmitter 101 generates, using a relatively simple encoding technique, LDPC codes based on parity check matrices (which facilitate efficient memory access during decoding) to communicate with the receiver 105. The transmitter 101 employs LDPC codes that can outperform concatenated turbo+RS (Reed-Solomon) codes, provided the block length is sufficiently large.

FIGS. 2A and 2B are diagrams of exemplary LDPC encoders deployed in the transmitter of FIG. 1. As seen in FIG. 2A, a transmitter 200 is equipped with an LDPC encoder 203 that accepts input from an information source 201 and outputs coded stream of higher redundancy suitable for error correction processing at the receiver 105. The information source 201 generates k signals from a discrete alphabet, X. LDPC codes are specified with parity check matrices. On the other hand, encoding LDPC codes require, in general, specifying the generator matrices. Even though it is possible to obtain generator matrices from parity check matrices using Gaussian elimination, the resulting matrix is no longer sparse and storing a large generator matrix can be complex.

Encoder 203 generates signals from alphabet Y to a modulator 205 using a simple encoding technique that makes use of only the parity check matrix by imposing structure onto the parity check matrix. Specifically, a restriction is placed on the parity check matrix by constraining certain portion of the matrix to be triangular. The construction of such a parity check matrix is described more fully below in FIG. 6. Such a restriction results in negligible performance loss, and therefore, constitutes an attractive trade-off.

Modulator 205 maps the encoded messages from encoder 203 to signal waveforms that are transmitted to a transmit antenna 207, which emits these waveforms over the communication channel 103. Accordingly, the encoded messages are modulated and distributed to a transmit antenna 207. The transmissions from the transmit antenna 207 propagate to a receiver, as discussed below.

FIG. 2B shows an LDPC encoder utilized with a Bose Chaudhuri Hocquenghem (BCH) encoder and a cyclic redundancy check (CRC) encoder, according to one embodiment of the present invention. Under this scenario, the codes generated by the LDPC encoder 203, along with the CRC encoder 209 and the BCH encoder 211, have a concatenated outer BCH code and inner low density parity check (LDPC) code. Furthermore, error detection is achieved using cyclic redundancy check (CRC) codes. The CRC encoder 209, in an exemplary embodiment, encodes using an 8-bit CRC code with generator polynomial (x⁵+x⁴+x³+x²+1)(x²+x+1)(x+1).

The LDPC encoder 203 systematically encodes an information block of size k_(ldpc), i=(i₀, i₁, . . . , i_(k) _(ldpc) ⁻¹) onto a codeword of size n_(ldpc), c=(i₀, i₁, . . . i_(k) _(ldpc) ⁻¹, p₀, p₁, . . . . P_(n) _(ldpc) _(−k) _(ldpc) ₋₁) The transmission of the codeword starts in the given order from i₀ and ends with p_(n) _(ldpc) _(-k) _(ldpc) ⁻¹. LDPC code parameters (n_(ldpc), k_(ldpc)) are given in Table 1 below. TABLE 1 LDPC Code Parameters (n_(ldpc), k_(ldpc)) LDPC Uncoded LDPC Coded Block Block Length Length Code Rate k_(ldpc) n_(ldpc) ½ 32400 64800 ⅔ 43200 64800 ¾ 48600 64800 ⅘ 51840 64800 ⅚ 54000 64800 ⅗ 38880 64800 8/9 57600 64800   9/10 58320 64800

The task of the LDPC encoder 203 is to determine n_(ldpc)−k_(ldpc) parity bits (p₀, p₁, . . . . P_(n) _(ldpc) _(−k) _(ldpc) ⁻¹) for every block of k_(ldpc) information bits, (i₀, i₁, . . . , i_(k) _(ldpc) ⁻¹). The procedure is as follows. First, the parity bits are initialized;

p₀=p₁=p₂= . . . =p_(n) _(ldpc) _(−k) _(ldpc) ⁼⁻¹=0. The first information bit, i₀, are accumulated at parity bit addresses specified in the first row of Tables 3 through 10. For example, for rate 2/3 (Table 3), the following results: p₀=p₀⊕i₀ p₁₀₄₉₁=p₁₀₄₉₁⊕i₀ p₁₆₀₄₃=p₀₁₆₄₃⊕i₀ p₅₀₆=p₅₀₆⊕i₀ p₁₂₈₂₆=p₁₂₈₂₆⊕i₀ p₈₀₆₅=p₈₀₆₅⊕i₀ p₈₂₂₆=p₈₂₂₆⊕i₀ p₂₇₆₇=p₂₇₆₇⊕i₀ p₂₄₀=p₂₄₀⊕i₀ p₁₈₆₇₃=p₁₈₆₇₃⊕i₀ p₉₂₇₉=p₉₂₇₉⊕i₀ p₁₀₅₇₉=p₁₀₅₇₉⊕i₀ p₂₀₉₂₈=p₂₀₉₂₈⊕i₀ (All additions are in GF(2)).

Then, for the next 359 information bits, i_(m), m=1, 2, . . . , 359, these bits are accumulated at parity bit addresses {x+m mod 360×q} mod(n_(ldpc)−k_(ldpc)), where x denotes the address of the parity bit accumulator corresponding to the first bit i₀, and q is a code rate dependent constant specified in Table 2. Continuing with the example, q=60 for rate 2/3. By way of example, for information bit i₁, the following operations are performed: p₆₀=p₆₀⊕i₁ p₁₀₅₅₁=p₁₀₅₅₁⊕i₁ p₁₆₁₀₃=p₁₀₆₁₀₃⊕i₁ p₅₆₆=p₅₆₆⊕i₁ p₁₂₈₈₆=p₁₂₈₈₆⊕i₁ p₈₁₂₅=p₈₁₂₅⊕i₁ p₈₂₈₆=p₈₂₈₆⊕i₁ p₂₈₂₇=p₂₈₂₇⊕i₁ p₃₀₀=p₃₀₀⊕i₁ p₁₈₇₃₃=p₁₈₇₃₃⊕i₁ p₉₃₃₉=p₉₃₃₉⊕i₁ p₁₀₆₃₉=p₁₀₆₃₉⊕i₁ p₂₀₉₈₈=p₂₀₉₈₈⊕i₁

For the 361^(st) information bit i₃₆₀, the addresses of the parity bit accumulators are given in the second row of the Tables 3 through 10. In a similar manner the addresses of the parity bit accumulators for the following 359 information bits i_(m), m=361, 362, . . . , 719 are obtained using the formula {x+m mod 360×q} mod(n_(ldpc)−k_(ldpc)), where x denotes the address of the parity bit accumulator corresponding to the information bit i₃₆₀, i.e., the entries in the second row of the Tables 3-10. In a similar manner, for every group of 360 new information bits, a new row from Tables 3 through 10 are used to find the addresses of the parity bit accumulators.

After all of the information bits are exhausted, the final parity bits are obtained as follows. First, the following operations are performed, starting with i=1 p _(i) =p _(i) ⊕p _(i−1) , i=1, 2, . . . , n _(ldpc) −k _(ldpc)−1.

Final content of p_(i), i=0, 1, . . . , n_(ldpc)−k_(ldpc)−1 is equal to the parity bit p_(i). TABLE 2 Code Rate q ⅔ 60 ⅚ 30 ½ 90 ¾ 45 ⅘ 36 ⅗ 72 8/9 20   9/10 18

TABLE 3 Address of Parity Bit Accumulators (Rate ⅔) 0 10491 16043 506 12826 8065 8226 2767 240 18673 9279 10579 20928 1 17819 8313 6433 6224 5120 5824 12812 17187 9940 13447 13825 18483 2 17957 6024 8681 18628 12794 5915 14576 10970 12064 20437 4455 7151 3 19777 6183 9972 14536 8182 17749 11341 5556 4379 17434 15477 18532 4 4651 19689 1608 659 16707 14335 6143 3058 14618 17894 20684 5306 5 9778 2552 12096 12369 15198 16890 4851 3109 1700 18725 1997 15882 6 486 6111 13743 11537 5591 7433 15227 14145 1483 3887 17431 12430 7 20647 14311 11734 4180 8110 5525 12141 15761 18661 18441 10569 8192 8 3791 14759 15264 19918 10132 9062 10010 12786 10675 9682 19246 5454 9 19525 9485 7777 19999 8378 9209 3163 20232 6690 16518 716 7353 10 4588 6709 20202 10905 915 4317 11073 13576 16433 368 3508 21171 11 14072 4033 19959 12608 631 19494 14160 8249 10223 21504 12395 4322 12 13800 14161 13 2948 9647 14 14693 16027 15 20506 11082 16 1143 9020 17 13501 4014 18 1548 2190 19 12216 21556 20 2095 19897 21 4189 7958 22 15940 10048 23 515 12614 24 8501 8450 25 17595 16784 26 5913 8495 27 16394 10423 28 7409 6981 29 6678 15939 30 20344 12987 31 2510 14588 32 17918 6655 33 6703 19451 34 496 4217 35 7290 5766 36 10521 8925 37 20379 11905 38 4090 5838 39 19082 17040 40 20233 12352 41 19365 19546 42 6249 19030 43 11037 19193 44 19760 11772 45 19644 7428 46 16076 3521 47 11779 21062 48 13062 9682 49 8934 5217 50 11087 3319 51 18892 4356 52 7894 3898 53 5963 4360 54 7346 11726 55 5182 5609 56 2412 17295 57 9845 20494 58 6687 1864 59 20564 5216 0 18226 17207 1 9380 8266 2 7073 3065 3 18252 13437 4 9161 15642 5 10714 10153 6 11585 9078 7 5359 9418 8 9024 9515 9 1206 16354 10 14994 1102 11 9375 20796 12 15964 6027 13 14789 6452 14 8002 18591 15 14742 14089 16 253 3045 17 1274 19286 18 14777 2044 19 13920 9900 20 452 7374 21 18206 9921 22 6131 5414 23 10077 9726 24 12045 5479 25 4322 7990 26 15616 5550 27 15561 10661 28 20718 7387 29 2518 18804 30 8984 2600 31 6516 17909 32 11148 98 33 20559 3704 34 7510 1569 35 16000 11692 36 9147 10303 37 16650 191 38 15577 18685 39 17167 20917 40 4256 3391 41 20092 17219 42 9218 5056 43 18429 8472 44 12093 20753 45 16345 12748 46 16023 11095 47 5048 17595 48 18995 4817 49 16483 3536 50 1439 16148 51 3661 3039 52 19010 18121 53 8968 11793 54 13427 18003 55 5303 3083 56 531 16668 57 4771 6722 58 5695 7960 59 3589 14630

TABLE 4 Address of Parity Bit Accumulators (Rate ⅚) 0 4362 416 8909 4156 3216 3112 2560 2912 6405 8593 4969 6723 1 2479 1786 8978 3011 4339 9313 6397 2957 7288 5484 6031 10217 2 10175 9009 9889 3091 4985 7267 4092 8874 5671 2777 2189 8716 3 9052 4795 3924 3370 10058 1128 9996 10165 9360 4297 434 5138 4 2379 7834 4835 2327 9843 804 329 8353 7167 3070 1528 7311 5 3435 7871 348 3693 1876 6585 10340 7144 5870 2084 4052 2780 6 3917 3111 3476 1304 10331 5939 5199 1611 1991 699 8316 9960 7 6883 3237 1717 10752 7891 9764 4745 3888 10009 4176 4614 1567 8 10587 2195 1689 2968 5420 2580 2883 6496 111 6023 1024 4449 9 3786 8593 2074 3321 5057 1450 3840 5444 6572 3094 9892 1512 10 8548 1848 10372 4585 7313 6536 6379 1766 9462 2456 5606 9975 11 8204 10593 7935 3636 3882 394 5968 8561 2395 7289 9267 9978 12 7795 74 1633 9542 6867 7352 6417 7568 10623 725 2531 9115 13 7151 2482 4260 5003 10105 7419 9203 6691 8798 2092 8263 3755 14 3600 570 4527 200 9718 6771 1995 8902 5446 768 1103 6520 15 6304 7621 16 6498 9209 17 7293 6786 18 5950 1708 19 8521 1793 20 6174 7854 21 9773 1190 22 9517 10268 23 2181 9349 24 1949 5560 25 1556 555 26 8600 3827 27 5072 1057 28 7928 3542 29 3226 3762 0 7045 2420 1 9645 2641 2 2774 2452 3 5331 2031 4 9400 7503 5 1850 2338 6 10456 9774 7 1692 9276 8 10037 4038 9 3964 338 10 2640 5087 11 858 3473 12 5582 5683 13 9523 916 14 4107 1559 15 4506 3491 16 8191 4182 17 10192 6157 18 5668 3305 19 3449 1540 20 4766 2697 21 4069 6675 22 1117 1016 23 5619 3085 24 8483 8400 25 8255 394 26 6338 5042 27 6174 5119 28 7203 1989 29 1781 5174 0 1464 3559 1 3376 4214 2 7238 67 3 10595 8831 4 1221 6513 5 5300 4652 6 1429 9749 7 7878 5131 8 4435 10284 9 6331 5507 10 6662 4941 11 9614 10238 12 8400 8025 13 9156 5630 14 7067 8878 15 9027 3415 16 1690 3866 17 2854 8469 18 6206 630 19 363 5453 20 4125 7008 21 1612 6702 22 9069 9226 23 5767 4060 24 3743 9237 25 7018 5572 26 8892 4536 27 853 6064 28 8069 5893 29 2051 2885 0 10691 3153 1 3602 4055 2 328 1717 3 2219 9299 4 1939 7898 5 617 206 6 8544 1374 7 10676 3240 8 6672 9489 9 3170 7457 10 7868 5731 11 6121 10732 12 4843 9132 13 580 9591 14 6267 9290 15 3009 2268 16 195 2419 17 8016 1557 18 1516 9195 19 8062 9064 20 2095 8968 21 753 7326 22 6291 3833 23 2614 7844 24 2303 646 25 2075 611 26 4687 362 27 8684 9940 28 4830 2065 29 7038 1363 0 1769 7837 1 3801 1689 2 10070 2359 3 3667 9918 4 1914 6920 5 4244 5669 6 10245 7821 7 7648 3944 8 3310 5488 9 6346 9666 10 7088 6122 11 1291 7827 12 10592 8945 13 3609 7120 14 9168 9112 15 6203 8052 16 3330 2895 17 4264 10563 18 10556 6496 19 8807 7645 20 1999 4530 21 9202 6818 22 3403 1734 23 2106 9023 24 6881 3883 25 3895 2171 26 4062 6424 27 3755 9536 28 4683 2131 29 7347 8027

TABLE 5 Address of Parity Bit Accumulators (Rate ½) 54 9318 14392 27561 26909 10219 2534 8597 55 7263 4635 2530 28130 3033 23830 3651 56 24731 23583 26036 17299 5750 792 9169 57 5811 26154 18653 11551 15447 13685 16264 58 12610 11347 28768 2792 3174 29371 12997 59 16789 16018 21449 6165 21202 15850 3186 60 31016 21449 17618 6213 12166 8334 18212 61 22836 14213 11327 5896 718 11727 9308 62 2091 24941 29966 23634 9013 15587 5444 63 22207 3983 16904 28534 21415 27524 25912 64 25687 4501 22193 14665 14798 16158 5491 65 4520 17094 23397 4264 22370 16941 21526 66 10490 6182 32370 9597 30841 25954 2762 67 22120 22865 29870 15147 13668 14955 19235 68 6689 18408 18346 9918 25746 5443 20645 69 29982 12529 13858 4746 30370 10023 24828 70 1262 28032 29888 13063 24033 21951 7863 71 6594 29642 31451 14831 9509 9335 31552 72 1358 6454 16633 20354 24598 624 5265 73 19529 295 18011 3080 13364 8032 15323 74 11981 1510 7960 21462 9129 11370 25741 75 9276 29656 4543 30699 20646 21921 28050 76 15975 25634 5520 31119 13715 21949 19605 77 18688 4608 31755 30165 13103 10706 29224 78 21514 23117 12245 26035 31656 25631 30699 79 9674 24966 31285 29908 17042 24588 31857 80 21856 27777 29919 27000 14897 11409 7122 81 29773 23310 263 4877 28622 20545 22092 82 15605 5651 21864 3967 14419 22757 15896 83 30145 1759 10139 29223 26086 10556 5098 84 18815 16575 2936 24457 26738 6030 505 85 30326 22298 27562 20131 26390 6247 24791 86 928 29246 21246 12400 15311 32309 18608 87 20314 6025 26689 16302 2296 3244 19613 88 6237 11943 22851 15642 23857 15112 20947 89 26403 25168 19038 18384 8882 12719 7093 0 14567 24965 1 3908 100 2 10279 240 3 24102 764 4 12383 4173 5 13861 15918 6 21327 1046 7 5288 14579 8 28158 8069 9 16583 11098 10 16681 28363 11 13980 24725 12 32169 17989 13 10907 2767 14 21557 3818 15 26676 12422 16 7676 8754 17 14905 20232 18 15719 24646 19 31942 8589 20 19978 27197 21 27060 15071 22 6071 26649 23 10393 11176 24 9597 13370 25 7081 17677 26 1433 19513 27 26925 9014 28 19202 8900 29 18152 30647 30 20803 1737 31 11804 25221 32 31683 17783 33 29694 9345 34 12280 26611 35 6526 26122 36 26165 11241 37 7666 26962 38 16290 8480 39 11774 10120 40 30051 30426 41 1335 15424 42 6865 17742 43 31779 12489 44 32120 21001 45 14508 6996 46 979 25024 47 4554 21896 48 7989 21777 49 4972 20661 50 6612 2730 51 12742 4418 52 29194 595 53 19267 20113

TABLE 6 Address of Parity Bit Accumulators (Rate ¾) 0 6385 7901 14611 13389 11200 3252 5243 2504 2722 821 7374 1 11359 2698 357 13824 12772 7244 6752 15310 852 2001 11417 2 7862 7977 6321 13612 12197 14449 15137 13860 1708 6399 13444 3 1560 11804 6975 13292 3646 3812 8772 7306 5795 14327 7866 4 7626 11407 14599 9689 1628 2113 10809 9283 1230 15241 4870 5 1610 5699 15876 9446 12515 1400 6303 5411 14181 13925 7358 6 4059 8836 3405 7853 7992 15336 5970 10368 10278 9675 4651 7 4441 3963 9153 2109 12683 7459 12030 12221 629 15212 406 8 6007 8411 5771 3497 543 14202 875 9186 6235 13908 3563 9 3232 6625 4795 546 9781 2071 7312 3399 7250 4932 12652 10 8820 10088 11090 7069 6585 13134 10158 7183 488 7455 9238 11 1903 10818 119 215 7558 11046 10615 11545 14784 7961 15619 12 3655 8736 4917 15874 5129 2134 15944 14768 7150 2692 1469 13 8316 3820 505 8923 6757 806 7957 4216 15589 13244 2622 14 14463 4852 15733 3041 11193 12860 13673 8152 6551 15108 8758 15 3149 11981 16 13416 6906 17 13098 13352 18 2009 14460 19 7207 4314 20 3312 3945 21 4418 6248 22 2669 13975 23 7571 9023 24 14172 2967 25 7271 7138 26 6135 13670 27 7490 14559 28 8657 2466 29 8599 12834 30 3470 3152 31 13917 4365 32 6024 13730 33 10973 14182 34 2464 13167 35 5281 15049 36 1103 1849 37 2058 1069 38 9654 6095 39 14311 7667 40 15617 8146 41 4588 11218 42 13660 6243 43 8578 7874 44 11741 2686 0 1022 1264 1 12604 9965 2 8217 2707 3 3156 11793 4 354 1514 5 6978 14058 6 7922 16079 7 15087 12138 8 5053 6470 9 12687 14932 10 15458 1763 11 8121 1721 12 12431 549 13 4129 7091 14 1426 8415 15 9783 7604 16 6295 11329 17 1409 12061 18 8065 9087 19 2918 8438 20 1293 14115 21 3922 13851 22 3851 4000 23 5865 1768 24 2655 14957 25 5565 6332 26 4303 12631 27 11653 12236 28 16025 7632 29 4655 14128 30 9584 13123 31 13987 9597 32 15409 12110 33 8754 15490 34 7416 15325 35 2909 15549 36 2995 8257 37 9406 4791 38 11111 4854 39 2812 8521 40 8476 14717 41 7820 15360 42 1179 7939 43 2357 8678 44 7703 6216 0 3477 7067 1 3931 13845 2 7675 12899 3 1754 8187 4 7785 1400 5 9213 5891 6 2494 7703 7 2576 7902 8 4821 15682 9 10426 11935 10 1810 904 11 11332 9264 12 11312 3570 13 14916 2650 14 7679 7842 15 6089 13084 16 3938 2751 17 8509 4648 18 12204 8917 19 5749 12443 20 12613 4431 21 1344 4014 22 8488 13850 23 1730 14896 24 14942 7126 25 14983 8863 26 6578 8564 27 4947 396 28 297 12805 29 13878 6692 30 11857 11186 31 14395 11493 32 16145 12251 33 13462 7428 34 14526 13119 35 2535 11243 36 6465 12690 37 6872 9334 38 15371 14023 39 8101 10187 40 11963 4848 41 15125 6119 42 8051 14465 43 11139 5167 44 2883 14521

TABLE 7 Address of Parity Bit Accumulators (Rate ⅘) 0 149 11212 5575 6360 12559 8108 8505 408 10026 12828 1 5237 490 10677 4998 3869 3734 3092 3509 7703 10305 2 8742 5553 2820 7085 12116 10485 564 7795 2972 2157 3 2699 4304 8350 712 2841 3250 4731 10105 517 7516 4 12067 1351 11992 12191 11267 5161 537 6166 4246 2363 5 6828 7107 2127 3724 5743 11040 10756 4073 1011 3422 6 11259 1216 9526 1466 10816 940 3744 2815 11506 11573 7 4549 11507 1118 1274 11751 5207 7854 12803 4047 6484 8 8430 4115 9440 413 4455 2262 7915 12402 8579 7052 9 3885 9126 5665 4505 2343 253 4707 3742 4166 1556 10 1704 8936 6775 8639 8179 7954 8234 7850 8883 8713 11 11716 4344 9087 11264 2274 8832 9147 11930 6054 5455 12 7323 3970 10329 2170 8262 3854 2087 12899 9497 11700 13 4418 1467 2490 5841 817 11453 533 11217 11962 5251 14 1541 4525 7976 3457 9536 7725 3788 2982 6307 5997 15 11484 2739 4023 12107 6516 551 2572 6628 8150 9852 16 6070 1761 4627 6534 7913 3730 11866 1813 12306 8249 17 12441 5489 8748 7837 7660 2102 11341 2936 6712 11977 18 10155 4210 19 1010 10483 20 8900 10250 21 10243 12278 22 7070 4397 23 12271 3887 24 11980 6836 25 9514 4356 26 7137 10281 27 11881 2526 28 1969 11477 29 3044 10921 30 2236 8724 31 9104 6340 32 7342 8582 33 11675 10405 34 6467 12775 35 3186 12198 0 9621 11445 1 7486 5611 2 4319 4879 3 2196 344 4 7527 6650 5 10693 2440 6 6755 2706 7 5144 5998 8 11043 8033 9 4846 4435 10 4157 9228 11 12270 6562 12 11954 7592 13 7420 2592 14 8810 9636 15 689 5430 16 920 1304 17 1253 11934 18 9559 6016 19 312 7589 20 4439 4197 21 4002 9555 22 12232 7779 23 1494 8782 24 10749 3969 25 4368 3479 26 6316 5342 27 2455 3493 28 12157 7405 29 6598 11495 30 11805 4455 31 9625 2090 32 4731 2321 33 3578 2608 34 8504 1849 35 4027 1151 0 5647 4935 1 4219 1870 2 10968 8054 3 6970 5447 4 3217 5638 5 8972 669 6 5618 12472 7 1457 1280 8 8868 3883 9 8866 1224 10 8371 5972 11 266 4405 12 3706 3244 13 6039 5844 14 7200 3283 15 1502 11282 16 12318 2202 17 4523 965 18 9587 7011 19 2552 2051 20 12045 10306 21 11070 5104 22 6627 6906 23 9889 2121 24 829 9701 25 2201 1819 26 6689 12925 27 2139 8757 28 12004 5948 29 8704 3191 30 8171 10933 31 6297 7116 32 616 7146 33 5142 9761 34 10377 8138 35 7616 5811 0 7285 9863 1 7764 10867 2 12343 9019 3 4414 8331 4 3464 642 5 6960 2039 6 786 3021 7 710 2086 8 7423 5601 9 8120 4885 10 12385 11990 11 9739 10034 12 424 10162 13 1347 7597 14 1450 112 15 7965 8478 16 8945 7397 17 6590 8316 18 6838 9011 19 6174 9410 20 255 113 21 6197 5835 22 12902 3844 23 4377 3505 24 5478 8672 25 4453 2132 26 9724 1380 27 12131 11526 28 12323 9511 29 8231 1752 30 497 9022 31 9288 3080 32 2481 7515 33 2696 268 34 4023 12341 35 7108 5553

TABLE 8 Address of Parity Bit Accumulators (Rate ⅗) 22422 10282 11626 19997 11161 2922 3122 99 5625 17064 8270 179 25087 16218 17015 828 20041 25656 4186 11629 22599 17305 22515 6463 11049 22853 25706 14388 5500 19245 8732 2177 13555 11346 17265 3069 16581 22225 12563 19717 23577 11555 25496 6853 25403 5218 15925 21766 16529 14487 7643 10715 17442 11119 5679 14155 24213 21000 1116 15620 5340 8636 16693 1434 5635 6516 9482 20189 1066 15013 25361 14243 18506 22236 20912 8952 5421 15691 6126 21595 500 6904 13059 6802 8433 4694 5524 14216 3685 19721 25420 9937 23813 9047 25651 16826 21500 24814 6344 17382 7064 13929 4004 16552 12818 8720 5286 2206 22517 2429 19065 2921 21611 1873 7507 5661 23006 23128 20543 19777 1770 4636 20900 14931 9247 12340 11008 12966 4471 2731 16445 791 6635 14556 18865 22421 22124 12697 9803 25485 7744 18254 11313 9004 19982 23963 18912 7206 12500 4382 20067 6177 21007 1195 23547 24837 756 11158 14646 20534 3647 17728 11676 11843 12937 4402 8261 22944 9306 24009 10012 11081 3746 24325 8060 19826 842 8836 2898 5019 7575 7455 25244 4736 14400 22981 5543 8006 24203 13053 1120 5128 3482 9270 13059 15825 7453 23747 3656 24585 16542 17507 22462 14670 15627 15290 4198 22748 5842 13395 23918 16985 14929 3726 25350 24157 24896 16365 16423 13461 16615 8107 24741 3604 25904 8716 9604 20365 3729 17245 18448 9862 20831 25326 20517 24618 13282 5099 14183 8804 16455 17646 15376 18194 25528 1777 6066 21855 14372 12517 4488 17490 1400 8135 23375 20879 8476 4084 12936 25536 22309 16582 6402 24360 25119 23586 128 4761 10443 22536 8607 9752 25446 15053 1856 4040 377 21160 13474 5451 17170 5938 10256 11972 24210 17833 22047 16108 13075 9648 24546 13150 23867 7309 19798 2988 16858 4825 23950 15125 20526 3553 11525 23366 2452 17626 19265 20172 18060 24593 13255 1552 18839 21132 20119 15214 14705 7096 10174 5663 18651 19700 12524 14033 4127 2971 17499 16287 22368 21463 7943 18880 5567 8047 23363 6797 10651 24471 14325 4081 7258 4949 7044 1078 797 22910 20474 4318 21374 13231 22985 5056 3821 23718 14178 9978 19030 23594 8895 25358 6199 22056 7749 13310 3999 23697 16445 22636 5225 22437 24153 9442 7978 12177 2893 20778 3175 8645 11863 24623 10311 25767 17057 3691 20473 11294 9914 22815 2574 8439 3699 5431 24840 21908 16088 18244 8208 5755 19059 8541 24924 6454 11234 10492 16406 10831 11436 9649 16264 11275 24953 2347 12667 19190 7257 7174 24819 2938 2522 11749 3627 5969 13862 1538 23176 6353 2855 17720 2472 7428 573 15036 0 18539 18661 1 10502 3002 2 9368 10761 3 12299 7828 4 15048 13362 5 18444 24640 6 20775 19175 7 18970 10971 8 5329 19982 9 11296 18655 10 15046 20659 11 7300 22140 12 22029 14477 13 11129 742 14 13254 13813 15 19234 13273 16 6079 21122 17 22782 5828 18 19775 4247 19 1660 19413 20 4403 3649 21 13371 25851 22 22770 21784 23 10757 14131 24 16071 21617 25 6393 3725 26 597 19968 27 5743 8084 28 6770 9548 29 4285 17542 30 13568 22599 31 1786 4617 32 23238 11648 33 19627 2030 34 13601 13458 35 13740 17328 36 25012 13944 37 22513 6687 38 4934 12587 39 21197 5133 40 22705 6938 41 7534 24633 42 24400 12797 43 21911 25712 44 12039 1140 45 24306 1021 46 14012 20747 47 11265 15219 48 4670 15531 49 9417 14359 50 2415 6504 51 24964 24690 52 14443 8816 53 6926 1291 54 6209 20806 55 13915 4079 56 24410 13196 57 13505 6117 58 9869 8220 59 1570 6044 60 25780 17387 61 20671 24913 62 24558 20591 63 12402 3702 64 8314 1357 65 20071 14616 66 17014 3688 67 19837 946 68 15195 12136 69 7758 22808 70 3564 2925 71 3434 7769

TABLE 9 Address of Parity Bit Accumulators (Rate 8/9) 0 6235 2848 3222 1 5800 3492 5348 2 2757 927 90 3 6961 4516 4739 4 1172 3237 6264 5 1927 2425 3683 6 3714 6309 2495 7 3070 6342 7154 8 2428 613 3761 9 2906 264 5927 10 1716 1950 4273 11 4613 6179 3491 12 4865 3286 6005 13 1343 5923 3529 14 4589 4035 2132 15 1579 3920 6737 16 1644 1191 5998 17 1482 2381 4620 18 6791 6014 6596 19 2738 5918 3786 0 5156 6166 1 1504 4356 2 130 1904 3 6027 3187 4 6718 759 5 6240 2870 6 2343 1311 7 1039 5465 8 6617 2513 9 1588 5222 10 6561 535 11 4765 2054 12 5966 6892 13 1969 3869 14 3571 2420 15 4632 981 16 3215 4163 17 973 3117 18 3802 6198 19 3794 3948 0 3196 6126 1 573 1909 2 850 4034 3 5622 1601 4 6005 524 5 5251 5783 6 172 2032 7 1875 2475 8 497 1291 9 2566 3430 10 1249 740 11 2944 1948 12 6528 2899 13 2243 3616 14 867 3733 15 1374 4702 16 4698 2285 17 4760 3917 18 1859 4058 19 6141 3527 0 2148 5066 1 1306 145 2 2319 871 3 3463 1061 4 5554 6647 5 5837 339 6 5821 4932 7 6356 4756 8 3930 418 9 211 3094 10 1007 4928 11 3584 1235 12 6982 2869 13 1612 1013 14 953 4964 15 4555 4410 16 4925 4842 17 5778 600 18 6509 2417 19 1260 4903 0 3369 3031 1 3557 3224 2 3028 583 3 3258 440 4 6226 6655 5 4895 1094 6 1481 6847 7 4433 1932 8 2107 1649 9 2119 2065 10 4003 6388 11 6720 3622 12 3694 4521 13 1164 7050 14 1965 3613 15 4331 66 16 2970 1796 17 4652 3218 18 1762 4777 19 5736 1399 0 970 2572 1 2062 6599 2 4597 4870 3 1228 6913 4 4159 1037 5 2916 2362 6 395 1226 7 6911 4548 8 4618 2241 9 4120 4280 10 5825 474 11 2154 5558 12 3793 5471 13 5707 1595 14 1403 325 15 6601 5183 16 6369 4569 17 4846 896 18 7092 6184 19 6764 7127 0 6358 1951 1 3117 6960 2 2710 7062 3 1133 3604 4 3694 657 5 1355 110 6 3329 6736 7 2505 3407 8 2462 4806 9 4216 214 10 5348 5619 11 6627 6243 12 2644 5073 13 4212 5088 14 3463 3889 15 5306 478 16 4320 6121 17 3961 1125 18 5699 1195 19 6511 792 0 3934 2778 1 3238 6587 2 1111 6596 3 1457 6226 4 1446 3885 5 3907 4043 6 6839 2873 7 1733 5615 8 5202 4269 9 3024 4722 10 5445 6372 11 370 1828 12 4695 1600 13 680 2074 14 1801 6690 15 2669 1377 16 2463 1681 17 5972 5171 18 5728 4284 19 1696 1459

TABLE 10 Address of Parity Bit Accumulators (Rate 9/10) 0 5611 2563 2900 1 5220 3143 4813 2 2481 834 81 3 6265 4064 4265 4 1055 2914 5638 5 1734 2182 3315 6 3342 5678 2246 7 2185 552 3385 8 2615 236 5334 9 1546 1755 3846 10 4154 5561 3142 11 4382 2957 5400 12 1209 5329 3179 13 1421 3528 6063 14 1480 1072 5398 15 3843 1777 4369 16 1334 2145 4163 17 2368 5055 260 0 6118 5405 1 2994 4370 2 3405 1669 3 4640 5550 4 1354 3921 5 117 1713 6 5425 2866 7 6047 683 8 5616 2582 9 2108 1179 10 933 4921 11 5953 2261 12 1430 4699 13 5905 480 14 4289 1846 15 5374 6208 16 1775 3476 17 3216 2178 0 4165 884 1 2896 3744 2 874 2801 3 3423 5579 4 3404 3552 5 2876 5515 6 516 1719 7 765 3631 8 5059 1441 9 5629 598 10 5405 473 11 4724 5210 12 155 1832 13 1689 2229 14 449 1164 15 2308 3088 16 1122 669 17 2268 5758 0 5878 2609 1 782 3359 2 1231 4231 3 4225 2052 4 4286 3517 5 5531 3184 6 1935 4560 7 1174 131 8 3115 956 9 3129 1088 10 5238 4440 11 5722 4280 12 3540 375 13 191 2782 14 906 4432 15 3225 1111 16 6296 2583 17 1457 903 0 855 4475 1 4097 3970 2 4433 4361 3 5198 541 4 1146 4426 5 3202 2902 6 2724 525 7 1083 4124 8 2326 6003 9 5605 5990 10 4376 1579 11 4407 984 12 1332 6163 13 5359 3975 14 1907 1854 15 3601 5748 16 6056 3266 17 3322 4085 0 1768 3244 1 2149 144 2 1589 4291 3 5154 1252 4 1855 5939 5 4820 2706 6 1475 3360 7 4266 693 8 4156 2018 9 2103 752 10 3710 3853 11 5123 931 12 6146 3323 13 1939 5002 14 5140 1437 15 1263 293 16 5949 4665 17 4548 6380 0 3171 4690 1 5204 2114 2 6384 5565 3 5722 1757 4 2805 6264 5 1202 2616 6 1018 3244 7 4018 5289 8 2257 3067 9 2483 3073 10 1196 5329 11 649 3918 12 3791 4581 13 5028 3803 14 3119 3506 15 4779 431 16 3888 5510 17 4387 4084 0 5836 1692 1 5126 1078 2 5721 6165 3 3540 2499 4 2225 6348 5 1044 1484 6 6323 4042 7 1313 5603 8 1303 3496 9 3516 3639 10 5161 2293 11 4682 3845 12 3045 643 13 2818 2616 14 3267 649 15 6236 593 16 646 2948 17 4213 1442 0 5779 1596 1 2403 1237 2 2217 1514 3 5609 716 4 5155 3858 5 1517 1312 6 2554 3158 7 5280 2643 8 4990 1353 9 5648 1170 10 1152 4366 11 3561 5368 12 3581 1411 13 5647 4661 14 1542 5401 15 5078 2687 16 316 1755 17 3392 1991

As regards the BCH encoder 211, the BCH code parameters are enumerated in Table 11. TABLE 11 BCH Uncoded Block BCH Error LDPC Code Length BCH Coded Block Correction Rate k_(bch) Length n_(bch) (bits) ½ 32208 32400 12 ⅔ 43040 43200 10 ¾ 48408 48600 12 ⅘ 51648 51840 12 ⅚ 53840 54000 10 ⅗ 38688 38880 12 8/9 57472 57600 8   9/10 58192 58320 8

It is noted that in the above table, n_(bch)=k_(ldpc).

The generator polynomial of the t error correcting BCH encoder 211 is obtained by multiplying the first t polynomials in the following list of Table 12: TABLE 12 g₁(x) 1 + x² + x³ + x⁵ + x¹⁶ g₂(x) 1 + x + x⁴ + x⁵ + x⁶ + x⁸ + x¹⁶ g₃(x) 1 + x² + x³ + x⁴ + x⁵ + x⁷ + x⁸ + x⁹ + x¹⁰ + x¹¹ + x¹⁶ g₄(x) 1 + x² + x⁴ + x⁶ + x⁹ + x¹¹ + x¹² + x¹⁴ + x¹⁶ g₅(x) 1 + x + x² + x³ + x⁵ + x⁸ + x⁹ + x¹⁰ + x¹¹ + x¹² + x¹⁶ g₆(x) 1 + x² + x⁴ + x⁵ + x⁷ + x⁸ + x⁹ + x¹⁰ + x¹² + x¹³ + x¹⁴ + x¹⁵ + x¹⁶ g₇(x) 1 + x² + x⁵ + x⁶ + x⁸ + x⁹ + x¹⁰ + x¹¹ + x¹³ + x¹⁵ + x¹⁶ g₈(x) 1 + x + x² + x⁵ + x⁶ + x⁸ + x⁹ + x¹² + x¹³ + x¹⁴ + x¹⁶ g₉(x) 1 + x⁵ + x⁷ + x⁹ + x¹⁰ + x¹¹ + x¹⁶ g₁₀(x) 1 + x + x² + x⁵ + x⁷ + x⁸ + x¹⁰ + x¹² + x¹³ + x¹⁴ + x¹⁶ g₁₁(x) 1 + x² + x³ + x⁵ + x⁹ + x¹¹ + x¹² + x¹³ + x¹⁶ g₁₂(x) 1 + x + x⁵ + x⁶ + x⁷ + x⁹ + x¹¹ + x¹² + x¹⁶

BCH encoding of information bits m=(m_(k) _(bch) ⁻¹, m_(k) _(bch) ⁻², . . . , m₁, m₀) onto a codeword c=(m_(k) _(bch) ⁻¹, m_(k) _(bch) ⁻², . . . , m₁, m₀, d_(n) _(bch) _(−k) _(bch) ⁻¹, d_(n) _(bch) _(−k) _(bch) ⁻², . . . , d₁, d₀) is achieved as follows. The message polynomial m(x)=m_(k) _(bch) ⁻¹x^(k) ^(bch) ⁻¹+m_(k) _(bch) ⁻²x^(k) ^(bch) ⁻²+ . . . +m₁x+m₀ is multiplied by x^(n) ^(bch) ^(−k) ^(bch) . Next, x^(n) ^(bch) ^(−k) ^(bch) m(x) divided by g(x). With d(x)=d_(n) _(bch) ^(−k) _(bch) ⁻¹x^(n) ^(bch) ^(−k) ^(bch) ⁻¹+ . . . +d₁x+d₀ as the remainder, the codeword polynomial is set as follows: c(x)=x^(n) ^(bch) ^(−k) ^(bch) m(x)+d(x).

The above LDPC codes, in an exemplary embodiment, can be used to variety of digital video applications, such as MPEG (Motion Pictures Expert Group) packet transmission.

FIG. 3 is a diagram of an exemplary receiver in the system of FIG. 1. At the receiving side, a receiver 300 includes a demodulator 301 that performs demodulation of received signals from transmitter 200. These signals are received at a receive antenna 303 for demodulation. After demodulation, the received signals are forwarded to a decoder 305, which attempts to reconstruct the original source messages by generating messages, X′, in conjunction with a bit metric generator 307. With non-Gray mapping, the bit metric generator 307 exchanges probability information with the decoder 305 back and forth (iteratively) during the decoding process, which is detailed in FIG. 10. Alternatively, if Gray mapping is used (according to one embodiment of the present invention), one pass of the bit metric generator is sufficient, in which further attempts of bit metric generation after each LDPC decoder iteration are likely to yield limited performance improvement; this approach is more fully described with respect to FIG. 11. To appreciate the advantages offered by the present invention, it is instructive to examine how LDPC codes are generated, as discussed in FIG. 4.

FIG. 4 is a diagram of a sparse parity check matrix, in accordance with an embodiment of the present invention. LDPC codes are long, linear block codes with sparse parity check matrix H_((n−k)xn). Typically the block length, n, ranges from thousands to tens of thousands of bits. For example, a parity check matrix for an LDPC code of length n=8 and rate 1/2 is shown in FIG. 4. The same code can be equivalently represented by the bipartite graph, per FIG. 5.

FIG. 5 is a diagram of a bipartite graph of an LDPC code of the matrix of FIG. 4. Parity check equations imply that for each check node, the sum (over GF (Galois Field)(2)) of all adjacent bit nodes is equal to zero. As seen in the figure, bit nodes occupy the left side of the graph and are associated with one or more check nodes, according to a predetermined relationship. For example, corresponding to check node m₁, the following expression exists n₁+n₄+n₅+n₈=0 with respect to the bit nodes.

Returning the receiver 303, the LDPC decoder 305 is considered a message passing decoder, whereby the decoder 305 aims to find the values of bit nodes. To accomplish this task, bit nodes and check nodes iteratively communicate with each other. The nature of this communication is described below.

From check nodes to bit nodes, each check node provides to an adjacent bit node an estimate (“opinion”) regarding the value of that bit node based on the information coming from other adjacent bit nodes. For instance, in the above example if the sum of n₄, n₅ and n₈ “looks like” 0 to m₁, then m₁ would indicate to n₁ that the value of n₁ is believed to be 0 (since n₁+n₄+n₅+n₈=0); otherwise m₁ indicate to n₁ that the value of n₁ is believed to be 1. Additionally, for soft decision decoding, a reliability measure is added.

From bit nodes to check nodes, each bit node relays to an adjacent check node an estimate about its own value based on the feedback coming from its other adjacent check nodes. In the above example n, has only two adjacent check nodes m₁ and m₃. If the feedback coming from m₃ to n₁ indicates that the value of n₁ is probably 0, then n₁ would notify m₁ that an estimate of n₁'s own value is 0. For the case in which the bit node has more than two adjacent check nodes, the bit node performs a majority vote (soft decision) on the feedback coming from its other adjacent check nodes before reporting that decision to the check node it communicates. The above process is repeated until all bit nodes are considered to be correct (i.e., all parity check equations are satisfied) or until a predetermined maximum number of iterations is reached, whereby a decoding failure is declared.

FIG. 6 is a diagram of a sub-matrix of a sparse parity check matrix, wherein the sub-matrix contains parity check values restricted to the lower triangular region, according to an embodiment of the present invention. As described previously, the encoder 203 (of FIGS. 2A and 2B) can employ a simple encoding technique by restricting the values of the lower triangular area of the parity check matrix. According to an embodiment of the present invention, the restriction imposed on the parity check matrix is of the form: H _((n−k)xn) =[A _((n−k)xk) B _((n−k)x(n−k))], where B is lower triangular.

Any information block i=(i₀, i₁, . . . , i_(k−1)) is encoded to a codeword c=(i₀, i₁, . . . , i_(k−1), p₀, p₁, . . . p_(n−k−1)) using Hc^(T)=0, and recursively solving for parity bits; for example, a ₀₀ i ₀ +a ₀₁ i ₁ + . . . +a _(0,k−1) i _(k−1) +p ₀=0

Solve p₀ a ₁₀ i ₀ +a ₁₁ i ₁ + . . . +a _(1,k−1) i _(k−1) +b ₁₀ p ₀ +p ₁=0

Solve p₁

-   -   and similarly for p₂, p₃, . . . , p_(n−k−1).

FIG. 7 is a graph showing performance between codes utilizing unrestricted parity check matrix (H matrix) versus restricted H matrix of FIG. 6. The graph shows the performance comparison between two LDPC codes: one with a general parity check matrix and the other with a parity check matrix restricted to be lower triangular to simplify encoding. The modulation scheme, for this simulation, is 8-PSK. The performance loss is within 0.1 dB. Therefore, the performance loss is negligible based on the restriction of the lower triangular H matrices, while the gain in simplicity of the encoding technique is significant. Accordingly, any parity check matrix that is equivalent to a lower triangular or upper triangular under row and/or column permutation can be utilized for the same purpose.

FIGS. 8A and 8B are, respectively, a diagram of a non-Gray 8-PSK modulation scheme, and a Gray 8-PSK modulation, each of which can be used in the system of FIG. 1. The non-Gray 8-PSK scheme of FIG. 8A can be utilized in the receiver of FIG. 3 to provide a system that requires very low Frame Erasure Rate (FER). This requirement can also be satisfied by using a Gray 8-PSK scheme, as shown in FIG. 8B, in conjunction with an outer code, such as Bose, Chaudhuri, and Hocquenghem (BCH), Hamming, or Reed-Solomon (RS) code.

Under this scheme, there is no need to iterate between the LDPC decoder 305 (FIG. 3) and the bit metric generator 307, which may employ 8-PSK modulation. In the absence of an outer code, the LDPC decoder 305 using Gray labeling exhibit an earlier error floor, as shown in FIG. 9 below.

FIG. 9 is a graph showing performance between codes utilizing Gray labeling versus non-Gray labeling of FIGS. 8A and 8B. The error floor stems from the fact that assuming correct feedback from LDPC decoder 305, regeneration of 8-PSK bit metrics is more accurate with non-Gray labeling since the two 8-PSK symbols with known two bits are further apart with non-Gray labeling. This can be equivalently seen as operating at higher Signal-to-Noise Ratio (SNR). Therefore, even though error asymptotes of the same LDPC code using Gray or non-Gray labeling have the same slope (i.e., parallel to each other), the one with non-Gray labeling passes through lower FER at any SNR.

On the other hand, for systems that do not require very low FER, Gray labeling without any iteration between LDPC decoder 305 and 8-PSK bit metric generator 307 may be more suitable because re-generating 8-PSK bit metrics before every LDPC decoder iteration causes additional complexity. Moreover, when Gray labeling is used, re-generating 8-PSK bit metrics before every LDPC decoder iteration yields only very slight performance improvement. As mentioned previously, Gray labeling without iteration may be used for systems that require very low FER, provided an outer code is implemented.

The choice between Gray labeling and non-Gray labeling depends also on the characteristics of the LDPC code. Typically, the higher bit or check node degrees, the better it is for Gray labeling, because for higher node degrees, the initial feedback from LDPC decoder 305 to 8-PSK (or similar higher order modulation) bit metric generator 307 deteriorates more with non-Gray labeling.

When 8-PSK (or similar higher order) modulation is utilized with a binary decoder, it is recognized that the three (or more) bits of a symbol are not received “equally noisy”. For example with Gray 8-PSK labeling, the third bit of a symbol is considered more noisy to the decoder than the other two bits. Therefore, the LDPC code design does not assign a small number of edges to those bit nodes represented by “more noisy” third bits of 8-PSK symbol so that those bits are not penalized twice.

FIG. 10 is a flow chart of the operation of the LDPC decoder using non-Gray mapping, according to an embodiment of the present invention. Under this approach, the LDPC decoder and bit metric generator iterate one after the other. In this example, 8-PSK modulation is utilized; however, the same principles apply to other higher modulation schemes as well. Under this scenario, it is assumed that the demodulator 301 outputs a distance vector, d, denoting the distances between received noisy symbol points and 8-PSK symbol points to the bit metric generator 307, whereby the vector components are as follows: $\begin{matrix} {d_{i} = {{- \frac{E_{s}}{N_{0}}}\left\{ {\left( {r_{x} - s_{i,x}} \right)^{2} + \left( {r_{y} - s_{i,y}} \right)^{2}} \right\}}} & {{i = 0},1,{\ldots\quad 7.}} \end{matrix}$

The 8-PSK bit metric generator 307 communicates with the LDPC decoder 305 to exchange a priori probability information and a posteriori probability information, which respectively are represented as u, and a. That is, the vectors u and a respectively represent a priori and a posteriori probabilities of log likelihood ratios of coded bits.

The 8-PSK bit metric generator 307 generates the a priori likelihood ratios for each group of three bits as follows. First, extrinsic information on coded bits is obtained: e _(j) =a _(j) −u _(j) j=0, 1, 2. Next, 8-PSK symbol probabilities, p_(i) i=0, 1, . . . , 7, are determined. *y _(j)=−ƒ(0,e _(j)) j=0, 1, 2 where ƒ(a,b)=max(a,b)+LUT _(ƒ)(a,b) with LUT _(ƒ() a,b)=ln(1+e ^(−|a−b|)) *x _(j) =y _(j) +e _(j) j=0, 1, 2 *p ₀ =x ₀ +x ₁ +x ₂ p ₄ =y ₀ +x ₁ +x ₂ p ₁ =x ₀ +x ₁ +y ₂ p ₅ =y ₀ +x ₁ +y ₂ p ₂ =x ₀ +y ₁ +x ₂ p ₆ =y ₀ +y ₁ +x ₂ p ₃ =x ₀ +y ₁ +y ₂ p ₇ =y ₀ +y ₁ +y ₂

Next, the bit metric generator 307 determines a priori log likelihood ratios of the coded bits as input to LDPC decoder 305, as follows: u ₀=ƒ(d ₀ +p ₀ ,d ₁ +p ₁ ,d ₂ +p ₂ ,d ₃ +p ₃)−ƒ(d ₄ +p ₄ ,d ₅ +p ₅ ,d ₆ +p ₆ ,d ₇ +p ₇)−e ₀ u ₁=ƒ(d ₀ +p ₀ ,d ₁ +p ₁ ,d ₄ +p ₄ ,d ₅ +p ₅)−ƒ(d ₂ +p ₂ ,d ₃ +p ₃ ,d ₆ +p ₆ ,d ₇ +p ₇)−e ₁ u ₂=ƒ(d ₀ +p ₀ ,d ₂ +p ₂ ,d ₄ +p ₄ ,d ₆ +p ₆)−ƒ(d ₁ +p ₁ ,d ₃ +p ₃ ,d ₅ +p ₅ ,d ₇ +p ₇)−e ₂

It is noted that the function ƒ(.) with more than two variables can be evaluated recursively; e.g. ƒ(a,b,c)=ƒ(ƒ(a,b),c).

The operation of the LDPC decoder 305 utilizing non-Gray mapping is now described. In step 1001, the LDPC decoder 305 initializes log likelihood ratios of coded bits, v, before the first iteration according to the following (and as shown in FIG. 12A): v_(n→k) _(i) =u_(n) , n=0, 1, . . . , N−1, i=1, 2, . . . , deg(bit node n) Here, v_(n→k) _(i) denotes the message that goes from bit node n to its adjacent check node k_(i), u_(n) denotes the demodulator output for the bit n and N is the codeword size.

In step 1003, a check node, k, is updated, whereby the input v yields the output w. As seen in FIG. 12B, the incoming messages to the check node k from its d_(c) adjacent bit nodes are denoted by v_(n) ₁ _(→k), v_(n) ₂ _(→k), . . . , v_(n) _(dc) _(→k). The goal is to compute the outgoing messages from the check node k back to d_(c) adjacent bit nodes. These messages are denoted by w_(k→n) ₁ , w_(k→n) ₂ , . . . w_(k→n) _(dc) , where w _(k→n) _(i) =g(v _(n) ₁ _(→k) ,v _(n) ₂ _(→k) , . . . , v _(n) _(i−1) _(→k) ,v _(n) _(i+1) _(→k) , . . . v _(n) _(dc) _(→k)). The function g( ) is defined as follows: g(a,b)=sign(a)×sign(b)×{min(|a|,|b|)}+LUT _(g)(a,b), where LUT_(g)(a,b)=ln(1+e^(−|a+b|))−ln(1+e^(−|a−b|)). Similar to function ƒ, function g with more than two variables can be evaluated recursively.

Next, the decoder 305, per step 1205, outputs a posteriori probability information (FIG. 12C), such that: $a_{n} = {u_{n} + {\sum\limits_{j}{w_{k_{j}\rightarrow n}.}}}$

Per step 1007, it is determined whether all the parity check equations are satisfied. If these parity check equations are not satisfied, then the decoder 305, as in step 1009, re-derives 8-PSK bit metrics and channel input u_(n). Next, the bit node is updated, as in step 1011. As shown in FIG. 14C, the incoming messages to the bit node n from its d_(v) adjacent check nodes are denoted by w_(k) ₁ _(→n), w_(k) ₂ _(→n), . . . , w_(k) _(dv) _(→n). The outgoing messages from the bit node n are computed back to d_(v) adjacent check nodes; such messages are denoted by v_(n→k) ₁ , v_(n→k) ₂ , . . . , v_(n→k) _(dv) , and computed as follows: $v_{n\rightarrow k_{i}} = {u_{n} + {\sum\limits_{j \neq i}w_{k_{j}\rightarrow n}}}$ In step 1013, the decoder 305 outputs the hard decision (in the case that all parity check equations are satisfied): ${\hat{c}}_{n} = \left\{ \begin{matrix} \begin{matrix} {0,} & {a_{n} \geq 0} \\ {1,} & {a_{n} < 0} \end{matrix} & {{{Stop}\quad{if}\quad H{\hat{c}}^{T}} = 0} \end{matrix} \right.$

The above approach is appropriate when non-Gray labeling is utilized. However, when Gray labeling is implemented, the process of FIG. 11 is executed.

FIG. 11 is a flow chart of the operation of the LDPC decoder of FIG. 3 using Gray mapping, according to an embodiment of the present invention. When Gray labeling is used, bit metrics are advantageously generated only once before the LDPC decoder, as re-generating bit metrics after every LDPC decoder iteration may yield nominal performance improvement. As with steps 1001 and 1003 of FIG. 10, initialization of the log likelihood ratios of coded bits, v, are performed, and the check node is updated, per steps 1101 and 1103. Next, the bit node n is updated, as in step 1105. Thereafter, the decoder outputs the a posteriori probability information (step 1107). In step 1109, a determination is made whether all of the parity check equations are satisfied; if so, the decoder outputs the hard decision (step 1111). Otherwise, steps 1103-1107 are repeated.

FIG. 13A is a flowchart of process for computing outgoing messages between the check nodes and the bit nodes using a forward-backward approach, according to an embodiment of the present invention. For a check node with d_(c) adjacent edges, the computation of d_(c)(d_(c)−1) and numerous g(.,.) functions are performed. However, the forward-backward approach reduces the complexity of the computation to 3(d_(c)−2), in which d_(c)−1 variables are stored.

Referring to FIG. 12B, the incoming messages to the check node k from d_(c) adjacent bit nodes are denoted by v_(n) ₁ _(→k), v_(n) ₂ _(→k), . . . , v_(n) _(dc) _(→k). It is desired that the outgoing messages are computed from the check node k back to d_(c) adjacent bit nodes; these outgoing messages are denoted by w_(k→n) ₁ , w_(k→n) ₂ , . . . , w_(k→n) _(dc) .

Under the forward-backward approach to computing these outgoing messages, forward variables, ƒ₁, ƒ₂, . . . , ƒ_(dc), are defined as follows: f₁ = v_(1 → k) f₂ = g(f₁, v_(2 → k)) f₃ = g(f₂, v_(3 → k)) $\begin{matrix} \vdots & \vdots & \vdots \end{matrix}$ f_(d  c) = g(f_(d  c − 1), v_(d  c → k)) In step 1301, these forward variables are computed, and stored, per step 1303.

Similarly, backward variables, b₁, b₂, . . . , b_(dc), are defined by the following: b_(d  c) = v_(d  c → k) b_(d  c − 1) = g(b_(d  c), v_(d  c − 1 → k)) $\begin{matrix} \vdots & \vdots & \vdots \end{matrix}$ b₁ = g(b₂, v_(1 → k)) In step 1305, these backward variables are then computed. Thereafter, the outgoing messages are computed, as in step 1307, based on the stored forward variables and the computed backward variables. The outgoing messages are computed as follows: w_(k→1)=b₂ w _(k→i) =g(ƒ_(i−1) ,b _(i+1)) i=2, 3, . . . , d _(c)−1 w _(k→dc)=ƒ_(dc−1)

Under this approach, only the forward variables, ƒ₂, ƒ₃, . . . , ƒ_(dc), are required to be stored. As the backward variables b_(i) are computed, the outgoing messages, w_(k→i), are simultaneously computed, thereby negating the need for storage of the backward variables.

The computation load can be further enhance by a parallel approach, as next discussed.

FIG. 13B is a flowchart of process for computing outgoing messages between the check nodes and the bit nodes using a parallel approach, according to an embodiment of the present invention. For a check node k with inputs v_(n) ₁ _(→k), v_(n) ₂ _(→k), . . . , v_(n) _(dc) _(→k) from d_(c) adjacent bit nodes, the following parameter is computed, as in step 1311: γ_(k) =g(v _(n) ₁ _(→k) ,v _(n) ₂ _(→k) , . . . , v _(n) _(dc) _(→k)).

It is noted that the g(.,.) function can also be expressed as follows: ${g\left( {a,b} \right)} = {\ln{\frac{1 + {\mathbb{e}}^{a + b}}{{\mathbb{e}}^{a} + {\mathbb{e}}^{b}}.}}$

Exploiting the recursive nature of the g(.,.) function, the following expression results: $\gamma_{k} = {{\ln\frac{1 + {\mathbb{e}}^{{g{({v_{n_{1}\rightarrow k},\ldots\quad,v_{n_{i - 1}\rightarrow k},v_{n_{i + 1}\rightarrow k},{\ldots\quad v_{n_{d\quad c}\rightarrow k}}})}} + v_{n_{i}\rightarrow k}}}{{\mathbb{e}}^{g{({v_{n_{1}\rightarrow k},\ldots\quad,v_{n_{i - 1}\rightarrow k},v_{n_{i + 1}\rightarrow k},\ldots,\quad v_{n_{d\quad c}\rightarrow k}})}} + {\mathbb{e}}^{v_{n_{i}\rightarrow k}}}} = {\ln\frac{1 + {\mathbb{e}}^{w_{k\rightarrow n_{i}} + v_{n_{i}\rightarrow k}}}{{\mathbb{e}}^{w_{k\rightarrow n_{i}}}{\mathbb{e}}^{v_{n_{i}\rightarrow k}}}}}$

Accordingly, w_(k→n) ₁ can be solved in the following manner: $w_{k\rightarrow n_{i}} = {{\ln\frac{{\mathbb{e}}^{v_{n_{i}\rightarrow k} + \gamma_{k}} - 1}{{\mathbb{e}}^{v_{n_{i}\rightarrow k} - \gamma_{k}} - 1}} - \gamma_{k}}$

The ln(.) term of the above equation can be obtained using a look-up table LUT_(x) that represents the function ln|e^(x)−1| (step 1313). Unlike the other look-up tables LUT_(f) or LUT_(g), the table LUT_(x) would likely requires as many entries as the number of quantization levels. Once γ_(k) is obtained, the calculation of w_(k→n) _(i) for all n_(i) can occur in parallel using the above equation, per step 1315.

The computational latency of γ_(k) is advantageously log₂(d_(c)).

FIGS. 14A-14C are graphs showing simulation results of LDPC codes generated in accordance with various embodiments of the present invention. In particular, FIGS. 14A-14C show the performance of LDPC codes with higher order modulation and code rates of 3/4 (QPSK, 1.485 bits/symbol), 2/3 (8-PSK, 1.980 bits/symbol), and 5/6 (8-PSK, 2.474 bits/symbol).

Two general approaches exist to realize the interconnections between check nodes and bit nodes: (1) a fully parallel approach, and (2) a partially parallel approach. In fully parallel architecture, all of the nodes and their interconnections are physically implemented. The advantage of this architecture is speed.

The fully parallel architecture, however, may involve greater complexity in realizing all of the nodes and their connections. Therefore with fully parallel architecture, a smaller block size may be required to reduce the complexity. In that case, for the same clock frequency, a proportional reduction in throughput and some degradation in FER versus Es/No performance may result.

The second approach to implementing LDPC codes is to physically realize only a subset of the total number of the nodes and use only these limited number of “physical” nodes to process all of the “functional” nodes of the code. Even though the LDPC decoder operations can be made extremely simple and can be performed in parallel, the further challenge in the design is how the communication is established between “randomly” distributed bit nodes and check nodes. The decoder 305 (of FIG. 3), according to one embodiment of the present invention, addresses this problem by accessing memory in a structured way, as to realize a seemingly random code. This approach is explained with respect to FIGS. 15A and 15B.

FIGS. 15A and 15B are diagrams of the top edge and bottom edge, respectively, of memory organized to support structured access as to realize randomness in LDPC coding, according to an embodiment of the present invention. Structured access can be achieved without compromising the performance of a truly random code by focusing on the generation of the parity check matrix. In general, a parity check matrix can be specified by the connections of the check nodes with the bit nodes. For example, the bit nodes can be divided into groups of a fixed size, which for illustrative purposes is 392. Additionally, assuming the check nodes connected to the first bit node of degree 3, for instance, are numbered as a, b and c, then the check nodes connected to the second bit node are numbered as a+p, b+p and c+p, the check nodes connected to the third bit node are numbered as a+2p, b+2p and c+2p etc.; where p=(number of check nodes)/392. For the next group of 392 bit nodes, the check nodes connected to the first bit node are different from a, b, c so that with a suitable choice of p, all the check nodes have the same degree. A random search is performed over the free constants such that the resulting LDPC code is cycle-4 and cycle-6 free. Because of the structural characteristics of the parity check matrix of the present invention, the edge information can stored to permit concurrent access to a group of relevant edge values during decoding.

In other words, the approach of the present invention facilitates memory access during check node and bit node processing. The values of the edges in the bipartite graph can be stored in a storage medium, such as random access memory (RAM). It is noted that for a truly random LDPC code during check node and bit node processing, the values of the edges would need to be accessed one by one in a random fashion. However, such a conventional access scheme would be too slow for a high data rate application. The RAM of FIGS. 15A and 15B are organized in a manner, whereby a large group of relevant edges can be fetched in one clock cycle; accordingly, these values are placed “together” in memory, according to a predetermined scheme or arrangement. It is observed that, in actuality, even with a truly random code, for a group of check nodes (and respectively bit nodes), the relevant edges can be placed next to one another in RAM, but then the relevant edges adjacent to a group of bit nodes (respectively check nodes) will be randomly scattered in RAM. Therefore, the “togetherness,” under the present invention, stems from the design of the parity check matrices themselves. That is, the check matrix design ensures that the relevant edges for a group of bit nodes and check nodes are simultaneously placed together in RAM.

As seen in FIGS. 15A and 15B, each box contains the value of an edge, which is multiple bits (e.g., 6). Edge RAM, according to one embodiment of the present invention, is divided into two parts: top edge RAM 1501 (FIG. 15A) and bottom edge RAM 1503 (FIG. 15B). Bottom edge RAM 1503 contains the edges between bit nodes of degree 2, for example, and check nodes. Top edge RAM contains the edges between bit nodes of degree greater than 2 and check nodes. Therefore, for every check node, 2 adjacent edges are stored in the bottom RAM 1503, and the rest of the edges are stored in the top edge RAM 1501. For example, the size of the top edge RAM 1501 and bottom edge RAM 1503 for various code rates are given in Table 14: TABLE 14 ½ ⅔ ¾ ⅚ Top Edge 400 × 392 440 × 392 504 × 392 520 × 392 RAM Bottom 160 × 392 110 × 392  72 × 392  52 × 392 Edge RAM

Based on Table 14, an edge RAM of size 576×392 is sufficient to store the edge metrics for all the code rates of 1/2, 2/3, 3/4, and 5/6.

As noted, under this exemplary scenario, a group of 392 bit nodes and 392 check nodes are selected for processing at a time. For 392 check node processing, q=d_(c)−2 consecutive rows are accessed from the top edge RAM 1501, and 2 consecutive rows from the bottom edge RAM 1503. The value of d_(c) depends on the specific code, for example d_(c)=7 for rate 1/2, d_(c)=10 for rate 2/3, d_(c)=16 for rate 3/4 and d_(c)=22 for rate 5/6 for the above codes. Of course other values of d_(c) for other codes are possible. In this instance, q+2 is the degree of each check node.

For bit node processing, if the group of 392 bit nodes has degree 2, their edges are located in 2 consecutive rows of the bottom edge RAM 1503. If the bit nodes have degree d>2, their edges are located in some d rows of the top edge RAM 1501. The address of these d rows can be stored in non-volatile memory, such as Read-Only Memory (ROM). The edges in one of the rows correspond to the first edges of 392 bit nodes, the edges in another row correspond to the second edges of 392 bit nodes, etc. Moreover for each row, the column index of the edge that belongs to the first bit node in the group of 392 can also be stored in ROM. The edges that correspond to the second, third, etc. bit nodes follow the starting column index in a “wrapped around” fashion. For example, if the j^(th) edge in the row belongs to the first bit node, then the (j+1)st edge belongs to the second bit node, (j+2)nd edge belongs to the third bit node, . . . , and (j−1)st edge belongs to the 392^(th) bit node.

With the organization shown in FIGS. 15A and 15B, speed of memory access is greatly enhanced during LDPC coding.

FIG. 16 illustrates a computer system upon which an embodiment according to the present invention can be implemented. The computer system 1600 includes a bus 1601 or other communication mechanism for communicating information, and a processor 1603 coupled to the bus 1601 for processing information. The computer system 1600 also includes main memory 1605, such as a random access memory (RAM) or other dynamic storage device, coupled to the bus 1601 for storing information and instructions to be executed by the processor 1603. Main memory 1605 can also be used for storing temporary variables or other intermediate information during execution of instructions to be executed by the processor 1603. The computer system 1600 further includes a read only memory (ROM) 1607 or other static storage device coupled to the bus 1601 for storing static information and instructions for the processor 1603. A storage device 1609, such as a magnetic disk or optical disk, is additionally coupled to the bus 1601 for storing information and instructions.

The computer system 1600 may be coupled via the bus 1601 to a display 1611, such as a cathode ray tube (CRT), liquid crystal display, active matrix display, or plasma display, for displaying information to a computer user. An input device 1613, such as a keyboard including alphanumeric and other keys, is coupled to the bus 1601 for communicating information and command selections to the processor 1603. Another type of user input device is cursor control 1615, such as a mouse, a trackball, or cursor direction keys for communicating direction information and command selections to the processor 1603 and for controlling cursor movement on the display 1611.

According to one embodiment of the invention, generation of LDPC codes is provided by the computer system 1600 in response to the processor 1603 executing an arrangement of instructions contained in main memory 1605. Such instructions can be read into main memory 1605 from another computer-readable medium, such as the storage device 1609. Execution of the arrangement of instructions contained in main memory 1605 causes the processor 1603 to perform the process steps described herein. One or more processors in a multi-processing arrangement may also be employed to execute the instructions contained in main memory 1605. In alternative embodiments, hard-wired circuitry may be used in place of or in combination with software instructions to implement the embodiment of the present invention. Thus, embodiments of the present invention are not limited to any specific combination of hardware circuitry and software.

The computer system 1600 also includes a communication interface 1617 coupled to bus 1601. The communication interface 1617 provides a two-way data communication coupling to a network link 1619 connected to a local network 1621. For example, the communication interface 1617 may be a digital subscriber line (DSL) card or modem, an integrated services digital network (ISDN) card, a cable modem, or a telephone modem to provide a data communication connection to a corresponding type of telephone line. As another example, communication interface 1617 may be a local area network (LAN) card (e.g. for Ethernet™ or an Asynchronous Transfer Model (ATM) network) to provide a data communication connection to a compatible LAN. Wireless links can also be implemented. In any such implementation, communication interface 1617 sends and receives electrical, electromagnetic, or optical signals that carry digital data streams representing various types of information. Further, the communication interface 1617 can include peripheral interface devices, such as a Universal Serial Bus (USB) interface, a PCMCIA (Personal Computer Memory Card International Association) interface, etc.

The network link 1619 typically provides data communication through one or more networks to other data devices. For example, the network link 1619 may provide a connection through local network 1621 to a host computer 1623, which has connectivity to a network 1625 (e.g. a wide area network (WAN) or the global packet data communication network now commonly referred to as the “Internet”) or to data equipment operated by service provider. The local network 1621 and network 1625 both use electrical, electromagnetic, or optical signals to convey information and instructions. The signals through the various networks and the signals on network link 1619 and through communication interface 1617, which communicate digital data with computer system 1600, are exemplary forms of carrier waves bearing the information and instructions.

The computer system 1600 can send messages and receive data, including program code, through the network(s), network link 1619, and communication interface 1617. In the Internet example, a server (not shown) might transmit requested code belonging to an application program for implementing an embodiment of the present invention through the network 1625, local network 1621 and communication interface 1617. The processor 1603 may execute the transmitted code while being received and/or store the code in storage device 169, or other non-volatile storage for later execution. In this manner, computer system 1600 may obtain application code in the form of a carrier wave.

The term “computer-readable medium” as used herein refers to any medium that participates in providing instructions to the processor 1603 for execution. Such a medium may take many forms, including but not limited to non-volatile media, volatile media, and transmission media. Non-volatile media include, for example, optical or magnetic disks, such as storage device 1609. Volatile media include dynamic memory, such as main memory 1605. Transmission media include coaxial cables, copper wire and fiber optics, including the wires that comprise bus 1601. Transmission media can also take the form of acoustic, optical, or electromagnetic waves, such as those generated during radio frequency (RF) and infrared (IR) data communications. Common forms of computer-readable media include, for example, a floppy disk, a flexible disk, hard disk, magnetic tape, any other magnetic medium, a CD-ROM, CDRW, DVD, any other optical medium, punch cards, paper tape, optical mark sheets, any other physical medium with patterns of holes or other optically recognizable indicia, a RAM, a PROM, and EPROM, a FLASH-EPROM, any other memory chip or cartridge, a carrier wave, or any other medium from which a computer can read.

Various forms of computer-readable media may be involved in providing instructions to a processor for execution. For example, the instructions for carrying out at least part of the present invention may initially be borne on a magnetic disk of a remote computer. In such a scenario, the remote computer loads the instructions into main memory and sends the instructions over a telephone line using a modem. A modem of a local computer system receives the data on the telephone line and uses an infrared transmitter to convert the data to an infrared signal and transmit the infrared signal to a portable computing device, such as a personal digital assistance (PDA) and a laptop. An infrared detector on the portable computing device receives the information and instructions borne by the infrared signal and places the data on a bus. The bus conveys the data to main memory, from which a processor retrieves and executes the instructions. The instructions received by main memory may optionally be stored on storage device either before or after execution by processor.

Accordingly, the various embodiments of the present invention provide an approach for encoding structured Low Density Parity Check (LDPC) codes. Structure of the LDPC codes is provided by restricting portion part of the parity check matrix to be lower triangular and/or satisfying other requirements such that the communication between bit nodes and check nodes of the decoder is simplified. Memory storing information representing the structured parity check matrix is accessed. The information is organized in tabular form, wherein each row represents occurrences of one values within a first column of a group of columns of the parity check matrix. The rows correspond to groups of columns of the parity check matrix, wherein subsequent columns within each of the groups are derived according to a predetermined operation (e.g., cyclic shift, addition, etc.). An LDPC coded signal based on the stored information representing the parity check matrix. According to one embodiment of the present invention, a Bose Chaudhuri Hocquenghem (BCH) encoder is utilized by the transmitter to encode an input signal using BCH codes, wherein the output LDPC coded signal corresponding to the input signal represents a code having an outer BCH code and an inner LDPC code. Further, a cyclic redundancy check (CRC) encoder is supplied to encode the input signal according to a CRC code. The above approach advantageously yields reduced complexity without sacrificing performance.

While the present invention has been described in connection with a number of embodiments and implementations, the present invention is not so limited but covers various obvious modifications and equivalent arrangements, which fall within the purview of the appended claims. 

1. A method of encoding, comprising: accessing memory storing information representing a structured parity check matrix of Low Density Parity Check (LDPC) codes, the information being organized in tabular Form, wherein each row represents occurrences of one values within a first column of a group of columns of the parity check matrix, the rows correspond to groups of columns of the parity check matrix, wherein subsequent columns within each of the groups are derived according to a predetermined operation; and outputting an LDPC coded signal based on the stored information representing the parity check matrix. 2.-33. (canceled) 